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Identifying functionally important cell states and structure within heterogeneous tumors remains a significant biological and computational challenge. Current clustering or trajectory-based models are ill-equipped to address the notion that cancer cells reside along a phenotypic continuum. We present Archetypal Analysis network (AAnet), a neural network that learns archetypal states within a phenotypic continuum in single-cell data. Unlike traditional archetypal analysis, AAnet learns archetypes in simplex-shaped neural network latent space. Using pre-clinical models and clinical breast cancers, AAnet resolves distinct cell states and processes, including cell proliferation, hypoxia, metabolism and immune interactions. Primary tumor archetypes are recapitulated in matched liver, lung and lymph node metastases. Spatial transcriptomics reveal archetypal organization within the tumor, and, intra-archetypal mirroring between cancer and adjacent stromal cells. AAnet identifies GLUT3 within the hypoxic archetype that proves critical for tumor growth and metastasis. AAnet is a powerful tool, capturing complex, functional cell states from multimodal data. 

Single-cell genomics has enabled the study of biological processes at an unprecedented scale and resolution. These studies were enabled by innovative data generation technologies coupled with emerging computational tools specialized for single-cell data. As single-cell technologies have become more prevalent, so has the development of new analysis tools, which has resulted in over 1,700 published algorithms1 (as of February 2024). Thus, there is an increasing need to continually evaluate which algorithm performs best in which context to inform best practices2,3 that evolve with the field. In many fields of quantitative science, public competitions and benchmarks address this need by evaluating state-of-the-art methods against known criteria, following the concept of a common task framework4. Here, we present Open Problems, a living, extensive, community-guided platform including 12 current single-cell tasks that we envisage raising standards for the selection, evaluation and development of methods in single-cell analysis.